feat: Expose remove_previous_ckpt option to training entry point an…

verl/trainer/config/ppo_trainer.yaml

@@ -170,4 +170,6 @@ trainer:
   test_freq: -1
   critic_warmup: 0
   default_hdfs_dir: null
+  remove_previous_ckpt_in_save: False
+  del_local_ckpt_after_load: False
   default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}

verl/trainer/ppo/ray_trainer.py

@@ -714,13 +714,19 @@ def _save_checkpoint(self):
 
         actor_remote_path = None if self.config.trainer.default_hdfs_dir is None else os.path.join(
             self.config.trainer.default_hdfs_dir, f'global_step_{self.global_steps}', 'actor')
-        self.actor_rollout_wg.save_checkpoint(actor_local_path, actor_remote_path, self.global_steps)
+        self.actor_rollout_wg.save_checkpoint(actor_local_path,
+                                              actor_remote_path,
+                                              self.global_steps,
+                                              remove_previous_ckpt=self.config.trainer.remove_previous_ckpt_in_save)
 
         if self.use_critic:
             critic_local_path = os.path.join(local_global_step_folder, 'critic')
             critic_remote_path = None if self.config.trainer.default_hdfs_dir is None else os.path.join(
                 self.config.trainer.default_hdfs_dir, f'global_step_{self.global_steps}', 'critic')
-            self.critic_wg.save_checkpoint(critic_local_path, critic_remote_path, self.global_steps)
+            self.critic_wg.save_checkpoint(critic_local_path,
+                                           critic_remote_path,
+                                           self.global_steps,
+                                           remove_previous_ckpt=self.config.trainer.remove_previous_ckpt_in_save)
 
         # save dataloader
         dataloader_local_path = os.path.join(local_global_step_folder, 'data.pt')
@@ -770,10 +776,12 @@ def _load_checkpoint(self):
         actor_path = os.path.join(global_step_folder, 'actor')
         critic_path = os.path.join(global_step_folder, 'critic')
         # load actor
-        self.actor_rollout_wg.load_checkpoint(actor_path)
+        self.actor_rollout_wg.load_checkpoint(actor_path,
+                                              del_local_after_load=self.config.trainer.del_local_ckpt_after_load)
         # load critic
         if self.use_critic:
-            self.critic_wg.load_checkpoint(critic_path)
+            self.critic_wg.load_checkpoint(critic_path,
+                                           del_local_after_load=self.config.trainer.del_local_ckpt_after_load)
 
         # load dataloader,
         # TODO: from remote not implemented yet

verl/utils/checkpoint/fsdp_checkpoint_manager.py

@@ -48,7 +48,7 @@ def __init__(self, model: FSDP, optimizer: torch.optim.Optimizer,
                  lr_scheduler: torch.optim.lr_scheduler.LRScheduler, tokenizer: PreTrainedTokenizer, *args, **kwargs):
         super().__init__(model, optimizer, lr_scheduler, tokenizer)
 
-    def load_checkpoint(self, path=None, del_local_after_load=True, *args, **kwargs):
+    def load_checkpoint(self, path=None, del_local_after_load=False, *args, **kwargs):
         if path is None:
             return
 
@@ -93,7 +93,7 @@ def load_checkpoint(self, path=None, del_local_after_load=True, *args, **kwargs)
         if self.lr_scheduler is not None:
             self.lr_scheduler.load_state_dict(lr_scheduler_state_dict)
 
-    def save_checkpoint(self, local_path: str, global_step: int, remove_previous_ckpt=True, *args, **kwargs):
+    def save_checkpoint(self, local_path: str, global_step: int, remove_previous_ckpt=False, *args, **kwargs):
         # record the previous global step
         self.previous_global_step = global_step
 

verl/workers/fsdp_workers.py

@@ -545,7 +545,7 @@ def compute_ref_log_prob(self, data: DataProto):
         return output
 
     @register(dispatch_mode=Dispatch.ONE_TO_ALL)
-    def save_checkpoint(self, local_path, hdfs_path=None, global_step=0):
+    def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False):
         # only support save and load ckpt for actor
         assert self._is_actor
         import torch
@@ -554,14 +554,17 @@ def save_checkpoint(self, local_path, hdfs_path=None, global_step=0):
                                      device_id=torch.cuda.current_device(),
                                      load_grad=self._is_offload_grad)
 
-        self.checkpoint_manager.save_checkpoint(local_path=local_path, hdfs_path=hdfs_path, global_step=global_step)
+        self.checkpoint_manager.save_checkpoint(local_path=local_path,
+                                                hdfs_path=hdfs_path,
+                                                global_step=global_step,
+                                                remove_previous_ckpt=remove_previous_ckpt)
 
         torch.distributed.barrier()
         if self._is_offload_param:
             offload_fsdp_param_and_grad(module=self.actor_module_fsdp, offload_grad=self._is_offload_grad)
 
     @register(dispatch_mode=Dispatch.ONE_TO_ALL)
-    def load_checkpoint(self, path, del_local_after_load=True):
+    def load_checkpoint(self, path, del_local_after_load=False):
         if self._is_offload_param:
             load_fsdp_param_and_grad(module=self.actor_module_fsdp,
                                      device_id=torch.cuda.current_device(),
@@ -821,14 +824,17 @@ def update_critic(self, data: DataProto):
         return output
 
     @register(dispatch_mode=Dispatch.ONE_TO_ALL)
-    def save_checkpoint(self, local_path, hdfs_path=None, global_step=0):
+    def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False):
         import torch
         if self._is_offload_param:
             load_fsdp_param_and_grad(module=self.critic_module,
                                      device_id=torch.cuda.current_device(),
                                      load_grad=self._is_offload_grad)
 
-        self.checkpoint_manager.save_checkpoint(local_path=local_path, hdfs_path=hdfs_path, global_step=global_step)
+        self.checkpoint_manager.save_checkpoint(local_path=local_path,
+                                                hdfs_path=hdfs_path,
+                                                global_step=global_step,
+                                                remove_previous_ckpt=remove_previous_ckpt)
 
         torch.distributed.barrier()
         if self._is_offload_param: